CN111541511B

CN111541511B - Communication interference signal identification method based on target detection in complex electromagnetic environment

Info

Publication number: CN111541511B
Application number: CN202010311576.5A
Authority: CN
Inventors: 付天晖; 杨晓乐; 王永斌; 翟琦; 陈斌; 罗勇; 李丽华; 冯士民
Original assignee: Naval University of Engineering PLA
Current assignee: Naval University of Engineering PLA
Priority date: 2020-04-20
Filing date: 2020-04-20
Publication date: 2022-08-16
Anticipated expiration: 2040-04-20
Also published as: CN111541511A

Abstract

The invention belongs to the technical field of signal identification, and discloses a communication interference signal identification method based on target detection in a complex electromagnetic environment, which is used for acquiring a time-frequency image of a calibrated interference signal; and carrying out target detection on the time-frequency image by using a target detection network, and predicting the rectangular bounding box coordinate of the region where the target is located. The invention can realize real-time accurate detection in a complex electromagnetic environment and has good generalization capability. 4 residual blocks of the invention are connected in series to form a backbone network, 2 detection modules are connected in series to finally obtain a feature diagram with the dimensions of (Batch,14, 128) and (Batch,28, 128), the feature diagram is fused with the output of the previous layer in the channel dimension after passing through an upper sampling layer, and the fused information is input into a detection layer to realize prediction. The trained target detection network is tested, and the recognition accuracy can reach 92.77%.

Description

Communication interference signal identification method based on target detection in complex electromagnetic environment

Technical Field

The invention belongs to the technical field of signal identification, and particularly relates to a communication interference signal identification method based on target detection in a complex electromagnetic environment.

Background

At present, communication interference signal identification is the basis for carrying out electronic countermeasure and ensuring the stability and reliability of a communication link. The battlefield electromagnetic environment of modern war is unusual complicated, and signals that communication systems such as many sides' shortwave, ultrashort wave, satellite produced and all kinds of electronic interference equipment produced make limited electromagnetic spectrum become unusual crowded, under this condition, accurately discerning fast in the current electromagnetic environment to the malicious interference signal of communication side be favorable to the assurance of battlefield electromagnetic environment of the operation personnel, adjust current communication strategy and deployment to the pertinence, promote communication reliability.

With the wide application of image processing technology and signal recognition technology, many signal recognition algorithms combining shape image processing and time-frequency images have appeared in recent years. For example: radar interference signals are identified based on time-frequency images, or Morse signals are automatically identified by processing methods such as image segmentation and morphological denoising; or the time-frequency analysis is combined with the statistical model to construct an underwater sound signal passive detection model based on the three time-frequency analysis methods, and the like. Because common communication interference signals have obvious geometric characteristics on a time-frequency domain, the problem of identifying the interference signals can be solved from the angle of image target detection. In reality it is common to artificially resolve these interfering objects in the time-frequency image. However, this approach has low detection efficiency, and when a plurality of communication signals and interference signals coexist in an environment, the accuracy and response speed of the manual detection method are difficult to meet the demand.

Through the above analysis, the problems and defects of the prior art are as follows: (1) in the prior art, the problem that the recognition rate of interference signals is low in a complex electromagnetic environment is solved, technical support cannot be provided for subsequent related researches in the field, and the practical application value is low.

(2) The prior art depends on manual treatment, the manual operation is complicated, the efficiency is lower, and the performance is reduced under the long-time working condition.

When the target is more, the prior art relying on manual treatment is difficult to deal with, and the problems of missing detection and the like are easy to occur.

The difficulty in solving the above problems and defects is: how to design the model structure enables the model to effectively identify the time-frequency images of common interference.

How to reduce the complexity of the model under the condition of ensuring the accuracy, so as to be capable of being deployed on equipment with limited computing capacity,

the significance for solving the problems and the defects is as follows: firstly, the method solves the problem of identifying the communication interference signals in the complex electromagnetic environment, can provide important information support for communication interference resistance decision, is easy to train, has low complexity and is convenient for actual deployment; secondly, under partial scenes, the interference signals can be distinguished from the time-frequency images instead of manual work; finally, the model can be quickly adapted to other scenes after retraining, can be used in the military field, has better application prospect in occasions such as radio monitoring, spectrum sharing and radio frequency identification, and has good compatibility.

Disclosure of Invention

In order to solve the problems in the prior art, the invention provides a communication interference signal identification method based on target detection in a complex electromagnetic environment. The identification model of the time-frequency image can partially or completely replace manual processing of communication interference conditions, and compared with manual operation, the performance of the identification model of the time-frequency image cannot be reduced under a long-time working condition.

The recognition model is a data-driven model, and can be quickly adapted to a new scene by replacing a data set.

The identification model based on the time-frequency image can be used for communication countermeasure, and can also be applied to the fields of daily radio frequency spectrum monitoring and commercial frequency spectrum sharing.

The invention is realized in such a way that a communication interference signal identification method based on target detection in a complex electromagnetic environment comprises the following steps:

acquiring a time-frequency image of a calibrated interference signal, and performing data enhancement processing;

secondly, performing target detection on the time-frequency image by using a target detection network, and acquiring a rectangular bounding box of an area where a prediction result is located;

and thirdly, detecting the time-frequency image in the actual complex environment by using the trained target detection network.

Further, in the first step, a USRP software Radio platform and GNU Radio software are used for generating communication interference signals of a specified type, RTL-SDR is used for receiving all signals in the environment under different backgrounds and presenting the signals in a time-frequency image form, a test platform is set up through hardware to obtain measured data, and a data set is made;

completing the calibration of the interference signal by using Labelme software, and acquiring the coordinate of an interference signal area;

and storing the coordinates of the time-frequency image and the interference signal area in a formatting mode.

Further, in the first step, the method for obtaining the time-frequency image of the calibrated interference signal includes:

converting time domain information of the received signal into time-frequency domain information by using short-time Fourier transform, and displaying the time-frequency domain information in a two-dimensional image form; the transformation formula is as follows:

where x (m) is the input signal and g (m) is the window function; transforming the time domain to a time-frequency domain by using an N-point fast Fourier transform, wherein N is 65536;

in the first step, a synthetic aperture radar-YOLO-Tiny network is built by utilizing a Pythrch deep learning framework, and the brightness, the contrast and the saturation of the image are randomly adjusted and enhanced by utilizing an Opencv computer vision library.

In the first step, the data format in the tag set is (x) ₁ ，y ₁ ，x ₂ ，y ₂ Class), which is converted by code into a specified format (class, x) required by the SAR-YOLO-Tiny model _center ，y _center Width, height). Where class represents the class of the object, x _center Representing normalized x-axis coordinates, y, of the center of the anchor frame _center And the normalized y-axis coordinate of the center of the anchor frame is represented, Width represents the normalized Width of the anchor frame, and Height represents the normalized Height of the anchor frame.

Further, in the second step, the obtained formatted data is used for training the SAR-YOLO-Tiny network;

the SAR-YOLO-Tiny network is constructed based on the YOLO-Tiny network, an anchor frame with a small aspect ratio is used, the size of the anchor frame is obtained by calculating the size of a boundary frame in an actual label through a K-means + + clustering algorithm, and the method comprises the following steps: 19 × 51, 8 × 66, 23 × 16, 10 × 29, 23 × 152, 50 × 83.

Further, the K-means + + clustering algorithm selects the K most representative sizes for prediction by iteration.

Further, a network module is constructed by using a deep learning framework, and comprises a residual error module, a convolution layer, a pooling layer and a detection layer, wherein the detection layer is used for calculating an error between a prediction result and a real label, and the error adjusts network parameters through back propagation until a training period is completed.

Further, in the second step, for the trunk network of the SAR-YOLO-Tiny, the traditional convolution layer in the original trunk network of the YOLO-Tiny is replaced by the residual block, and the shallow feature is combined for detection; the method comprises the following steps:

using a residual block containing 1 x 1 and 3 x 3 convolution kernels;

and (3) fusing the characteristic diagram after 5 times of pooling with the characteristic diagram after 2 times of upsampling, fusing the characteristic diagram after 4 times of pooling with the characteristic diagram after 3 times of pooling, and detecting by utilizing shallow and deep characteristics.

Further, in the third step, the time-frequency image is used for testing the trained SAR-YOLO-Tiny target detection network, and the average precision of the performance evaluation index mean value comprises the following steps:

in the formula, p (k) represents the accuracy of the network after k pictures are input, and Δ r (k) represents the change of the recall rate after the k picture is read.

Another object of the present invention is to provide a program storage medium for receiving user input, the stored computer program enabling an electronic device to execute the method for identifying a communication interference signal based on a time-frequency image in a complex electromagnetic environment.

Another object of the present invention is to provide a computer program product stored on a computer-readable medium, which includes a computer-readable program for providing a user input interface to implement the method for identifying a communication interference signal based on a time-frequency image in a complex electromagnetic environment when the computer program product is executed on an electronic device.

Another object of the present invention is to provide an electronic jamming device for performing the method for identifying a communication jamming signal based on a time-frequency image in a complex electromagnetic environment.

By combining all the technical schemes, the invention has the advantages and positive effects that:

the communication interference signal identification method based on target detection in the complex electromagnetic environment provided by the invention obtains the time-frequency image of the calibrated interference signal; and carrying out target detection on the time-frequency image by using an SAR-YOLO-Tiny target detection network, and acquiring a rectangular bounding box of the area where the prediction result is located. Meanwhile, the model has higher convergence rate, and is convenient to retrain to adapt to a new scene.

The method is based on the rule that time-frequency images of different types of communication interference signals have different visual characteristics, utilizes a YOLO-Tiny target detection network as a basis, improves network parameters and a structure aiming at a communication interference signal identification scene, solves the identification problem in a complex electromagnetic environment, provides an idea for the subsequent related research in the field, and has a high practical application value.

The effects and advantages obtained by combining experimental or experimental data with the prior art are: from FIG. 5, it can be seen that the SAR-YOLO-Tiny loss function falls faster than the original model YOLO-Tiny and the RadioyOLO model in radio Signal identification based on deep learning of images. FIG. 6 shows that in the training process, the rising speed of the SAR-YOLO-Tiny model accuracy rate is higher than that of YOLO-Tiny and RadiOYOLO. After 400 times of iterative training, the accuracy rate is close to 98%. The test result of the model is shown in fig. 7, and the test is performed every 6 training periods, and the test index is the map (mean Average precision). The index is used for measuring the comprehensive performance of the target detection network. The mAP value of the SAR-YOLO-Tiny model is obviously increased after 42 periods of training, and is higher than that of the control model. In combination, the SAR-YOLO-Tiny model is superior to a control model in the aspects of detection effectiveness and model complexity.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.

Fig. 1 is a flowchart of a communication interference signal identification method based on target detection in a complex electromagnetic environment according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of a time-frequency image of an FSK interfering signal according to an embodiment of the present invention.

Fig. 3 is a diagram of a residual block provided in an embodiment of the present invention.

Fig. 4 is a schematic diagram of a SAR-YOLO-Tiny network structure using a residual block according to an embodiment of the present invention.

Fig. 5 is a graph of the variation of the loss function during the training process of the present invention.

FIG. 6 is a graph showing the variation of the average accuracy of the model during the training process of the present invention.

FIG. 7 shows the variation of the average accuracy mAP with the training period in the testing process of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

In the prior art, the problem that the recognition rate of interference signals is low in a complex electromagnetic environment is solved, technical support cannot be provided for subsequent related researches in the field, and the practical application value is low.

In view of the problems in the prior art, the present invention provides a method for identifying a communication interference signal based on target detection in a complex electromagnetic environment, which is described in detail below with reference to the accompanying drawings.

As shown in fig. 1, a method for identifying a communication interference signal based on target detection in a complex electromagnetic environment includes:

s101, an interference transmitter is constructed by using USRP and GNU Radio software, a communication interference signal is transmitted, an RTL-SDR receiver is used for collecting signals, and the collected signals are presented in a time-frequency image stream form through short-time Fourier change.

S102, frame extraction is carried out on the time-frequency image stream, and a rectangular frame is used as a label to mark a target area. And training the SAR-YOLO-Tiny network model by using the time-frequency images and the corresponding labels.

S103, testing the trained SAR-YOLO-Tiny network by using the test set picture.

In step S101, the communication interference signal includes: tone interference, FSK interference, swept frequency interference, and wideband noise interference.

In step S101, an RTL-SDR receiver is used to receive monophonic interference, FSK interference, frequency sweep interference, and broadband noise interference signals in a complex electromagnetic environment.

The receiving frequency band comprises a short wave frequency band and an ultrashort wave frequency band, and an FM (frequency modulation) signal and an aviation radio signal are used as backgrounds, so that a communication interference signal is in a complex environment with various signals.

The time domain information of the received signal is converted into time-frequency domain information, namely a two-dimensional image, by using short-time Fourier transform. The transformation formula is as follows:

where x (m) is the input signal and g (m) is the window function. The time domain to time domain transform is performed using an N-point fast fourier transform, where N is 65536.

In step S102, the SAR-YOLO-Tiny uses the original backbone network of YOLO-Tiny as the backbone structure.

In step S102, aiming at a detection layer in the SAR-YOLO-Tiny network, a small-aspect-ratio anchor frame is used as a prior boundary frame. The size of the small aspect ratio anchor frame is obtained by a clustering algorithm.

The width and height of the anchor frame with the small aspect ratio respectively correspond to the signal bandwidth and the duration, and normalization processing is carried out relative to the width and height of the time-frequency image.

The K-means + + clustering algorithm was used to estimate the most representative K low aspect ratio anchor boxes.

In step S102, the present invention needs to predict the actual target area using a bounding box of a fixed size, and the size of the bounding box is obtained by a K-means + + clustering algorithm. The algorithm selects the k most representative dimensions for prediction by continually iterating, including the steps of:

taking the width and height data of each bounding box as a point, firstly, performing initialization clustering on all the points to obtain 6 clustering centers, wherein the principle of determining the clustering centers is that each clustering center is as far away as possible.

And calculating the distance between all the remaining points and the current point according to a distance formula, wherein the distance calculation formula is as follows:

Distance＝1-box_iou(box,centrcid)

wherein Distance represents Distance, box _ iou represents intersection ratio calculation function, box represents one of the remaining points, and centroid represents initialized clustering center. The box _ iou is calculated as follows:

acquiring the coordinate x of the upper left corner of the box on the x axis ₁ And length len on the shaft _x1 And acquiring the coordinate y of the upper left corner of the box on the y axis ₁ And length len on the shaft _y1 And the same parameter x of centrcid ₂ ，y ₂ ，len _x2 ，len _y2 . And calculating the ratio of the intersection area to the union area of the two bounding boxes according to the 8 parameters.

And sequentially calculating the distances between all the box and the existing clustering centers, and selecting the distance between the box and the nearest clustering center. And (3) setting the number of the boxes participating in the selection of the clustering center as m, and calculating the probability of selecting each box as the clustering center according to the following formula:

list P [ (box) _i )]A distribution law of randomly generating a sum of 01, the box corresponding to the probability interval where the number is located is the next cluster center. The width and height of the 6 cluster centers obtained by calculation are the sizes of the 6 prior bounding boxes.

In step S102, a residual error block is used to replace a conventional convolution layer in the SAR-YOLO-Tiny trunk structure. And the detection layer carries out detection based on the shallow characteristic map and the deep characteristic map.

In the step S102, a Pyroch deep learning framework is used for constructing an SAR-YOLO-Tiny network, and the SAR-YOLO-Tiny network is composed of a residual block, a feature fusion layer and a detection layer. The method comprises the following steps:

the residual block is constructed using a 3 × 3 convolution kernel, a 1 × 1 convolution kernel, and a LeakyReLU activation function.

Each residual block is followed by a maximum pooling layer of step 2, resulting in an output feature size of 1/2.

The 4 residual blocks, a plurality of convolution layers and pooling layers are connected in series to form a backbone network, 5 times of pooling finally obtains a feature map of (Batch,7, 128), and the feature map is fused with the output of the 4 th residual block in the channel dimension after passing through an upsampling layer. And (Batch,14, 128) feature maps are finally obtained through 4 times of pooling, the feature maps are fused with the output of the 3 rd residual block in the channel dimension after passing through an upsampling layer, and the fused information is input into a detection module to be used for calculating a loss function.

The upsampling layer adopts nearest neighbor interpolation, and the upsampling multiple is 2.

The number of channels of the input feature map of the detection layer is 30.

In step S103, the trained SAR-YOLO-Tiny target detection network is tested by using a time-frequency image, and the average accuracy of performance evaluation indexes comprises the following steps:

The trained target detection network is tested, and the average detection precision of the mean value can reach 92.77%.

Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention may be implemented by software plus a necessary hardware platform, and may also be implemented by hardware entirely. With this understanding in mind, all or part of the technical solutions of the present invention that contribute to the background can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments or some parts of the embodiments of the present invention.

The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.

Claims

1. A communication interference signal identification method based on target detection in a complex electromagnetic environment is characterized by comprising the following steps:

secondly, extracting frames of the time-frequency image stream, and marking a target area by using a rectangular frame as a label; training an SAR-YOLO-Tiny network model by using the time-frequency image and the corresponding label; the SAR-YOLO-Tiny network model is a network based on an anchor frame with a small aspect ratio;

and step three, testing the trained SAR-YOLO-Tiny network model by using the test set picture.

2. The method for identifying the communication interference signal based on the target detection in the complex electromagnetic environment according to claim 1, wherein in the first step, a USRP software Radio platform and GNU Radio software are used for generating the communication interference signal of a specified type, RTL-SDR is used for receiving all signals in the environment under different backgrounds and presenting the signals in a time-frequency image form, a test platform is built through hardware for obtaining measured data, and a data set is made;

3. The method for identifying communication interference signals based on target detection in complex electromagnetic environment according to claim 1, wherein in the first step, the method for obtaining the time-frequency image of the calibrated interference signals comprises:

in the first step, a Synthetic Aperture Radar (SAR) -YOLO-Tiny network model is built by using a Pythrch deep learning framework, and the brightness, the contrast and the saturation of the image are randomly adjusted by using an Opencv computer vision library to enhance the acquired image.

4. The method for identifying communication interference signals based on target detection in complex electromagnetic environment according to claim 1, wherein in the second step, the obtained formatted data is used to train a SAR-YOLO-Tiny network model;

the SAR-YOLO-Tiny network model is constructed on the basis of a YOLO-Tiny network, and an anchor frame with a small aspect ratio is used, wherein the anchor frame comprises 19 × 51, 8 × 66, 23 × 16, 10 × 29, 23 × 152 and 50 × 83, and an actual rectangular target area is estimated;

the size of the anchor frame with the small aspect ratio is obtained by calculating the size of a boundary frame in an actual label through a K-means + + clustering algorithm;

the SAR-YOLO-Tiny network model comprises a residual error module, a convolution layer, a pooling layer and a detection layer, wherein the detection layer is used for calculating an error between a prediction result and a real label, and the error adjusts network parameters through back propagation until a training period is completed.

5. The method for identifying communication interference signals based on target detection in the complex electromagnetic environment as claimed in claim 4, characterized in that the K-means + + clustering algorithm selects K most representative dimensions for prediction by continuous iteration, and the specific implementation manner is as follows:

taking the width and height data of each bounding box as a point, performing initialization clustering on all the points to obtain 6 clustering centers, and calculating the distance between the remaining all points and the current point according to a distance formula, wherein the distance calculation formula is as follows:

Distance＝1-box_iou(box,centrcid)

wherein Distance represents Distance, box _ iou represents an intersection-to-parallel ratio calculation function, box represents one of the remaining points, and centroid represents an initialized clustering center;

the box _ iou calculating step comprises the following steps:

acquiring the coordinate x of the upper left corner of the box on the x axis ₁ And length len on the shaft _x1 And acquiring the coordinate y of the upper left corner of the box on the y axis ₁ And length len on the shaft _y1 And the same parameter x of centrcid ₂ ，y ₂ ，len _x2 ，len _y2 Calculating the ratio of the intersection area to the union area of the two bounding boxes according to the 8 parameters;

sequentially calculating the distances between all the box and the existing clustering centers, and selecting the distance between the box and the nearest clustering center; and (3) setting the number of the boxes participating in the selection of the clustering center as m, and calculating the probability of selecting each box as the clustering center according to the following formula:

list P [ (box) _i )]Randomly generating a number between 0 and 1, wherein a box corresponding to a probability interval where the number is located is a next clustering center; the width and height of the 6 cluster centers are calculated as the size of the 6 prior bounding boxes.

6. The method for identifying communication interference signals based on target detection in complex electromagnetic environment as claimed in claim 1, wherein in the step three, for the backbone network of the SAR-YOLO-Tiny network model, the testing process includes the steps of:

using residual blocks containing 1 × 1 and 3 × 3 convolution kernels;

7. The method for identifying communication interference signals based on target detection in complex electromagnetic environment according to claim 1, wherein in the third step, a time-frequency image is used to test the target detection network of the trained SAR-YOLO-Tiny network model, and the average accuracy of the performance evaluation index comprises:

in the formula, p (k) represents the accuracy of the network after k pictures are input, and Δ r (k) represents the change of the recall rate after the k-th picture is read.

8. A program storage medium for receiving user input, the stored computer program enabling an electronic device to execute the method for identifying a communication interference signal based on object detection in a complex electromagnetic environment according to any one of claims 1 to 7.